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An Efficient Selective Perceptual-Based Super-Resolution Estimator

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4 Author(s)
Karam, L.J. ; Sch. of Electr., Comput., & Energy Eng., Arizona State Univ., Tempe, AZ, USA ; Sadaka, N.G. ; Ferzli, R. ; Ivanovski, Z.A.

In this paper, a selective perceptual-based (SELP) framework is presented to reduce the complexity of popular super-resolution (SR) algorithms while maintaining the desired quality of the enhanced images/video. A perceptual human visual system model is proposed to compute local contrast sensitivity thresholds. The obtained thresholds are used to select which pixels are super-resolved based on the perceived visibility of local edges. Processing only a set of perceptually significant pixels reduces significantly the computational complexity of SR algorithms without losing the achievable visual quality. The proposed SELP framework is integrated into a maximum-a posteriori-based SR algorithm as well as a fast two-stage fusion-restoration SR estimator. Simulation results show a significant reduction on average in computational complexity with comparable signal-to-noise ratio gains and visual quality.

Published in:

Image Processing, IEEE Transactions on  (Volume:20 ,  Issue: 12 )

Date of Publication:

Dec. 2011

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